A number of known target tracking methods are described in the book “Design and Analysis of Modern Tracking Systems” by Samuel Blackman and Robert Popoli, published by Artech House (1999). One particular class of tracking methods use multiple model algorithms. These advanced tracking algorithms are appropriate where the target dynamics may vary between a number of dynamic regimes. For example, the target may be an aircraft that can fly in a straight line or perform high-G manoeuvres. Multiple model techniques allow the aircraft to be accurately tracked when it transitions between these two dynamic regimes, and can be extended to allow for any number of dynamic regimes. Essentially, a number of tracking algorithms, each applying a different dynamics model to track the target, are run in parallel, and the output of the multiple model algorithms is a combination of the predictions of each tracking algorithm. One known example of a multiple model tracking algorithm is the interacting multiple models (IMM) algorithm, described by Blackman and Popoli. The IMM algorithm is a particularly efficient multiple model tracking algorithm.
Often, it is desirable to track a target using a number of sensors, so that the target can be tracked over a wider area, or so that a number of sensors of different type can be used. In order for optimal tracking to be achieved in such situations, it is necessary for data from all the sensors to be fused. A number of network architectures suitable for such fusion are known. These architectures can be classified as centralised, distributed, or decentralised.
Most tracking methods are implemented using a centralised or distributed architecture. Centralised architectures are those in which the data that is being fused is sent to a central processing facility for fusion. The central facility processes all the data in order to output a target track. Distributed architectures differ in that the central fusion process may place some computational load on remote units. However, a central processing facility must still exist in the system in order for the results of the distributed processing to be combined. Both centralised and distributed architectures are therefore vulnerable to the loss of the central processing facility, since such a loss leads inevitably to a catastrophic failure of the entire system. Furthermore, neither centralised nor distributed architectures are scaleable, firstly because, as the size of the network grows, the computational load placed on the central processing facility increases rapidly, and secondly because the quantity of data that must be communicated to and from the central processing facility increases rapidly with the size of the network. The size of the network is thus limited by available bandwidth for communication, and the computational power of the central processing facility.
Decentralised architectures are known, for example from the paper “Data Fusion in Decentralised Sensing Networks” by Hugh Durrant-Whyte and Mike Sterns, published in the Proceedings of the 4th International Conference on Information Fusion, Canada 2001. There is no central processing facility in a decentralised network. Each node is able to form a global estimate based on local sensor observations and information communicated to it by selected other nodes in the network. Normally, these other nodes will be adjacent or nearest neighbour nodes. In contrast to centralised and distributed architectures, no sensing, processing or communication component is critical to the operation of a network having a decentralised architecture. Thus failure of any single element results in only an incremental decrease in performance, rather than a catastrophic system failure as would happen in a network having a distributed or centralised architecture. Furthermore, no node requires knowledge of the global network topology, and the system can therefore be scaled simply by connecting new sensing nodes to the system. In contrast to distributed and centralised systems, the computational and bandwidth requirements do not increase with increasing network size. Communication is managed on a node-to-node basis, rather than requiring one or more nodes to broadcast across the network.
A decentralised network is characterised by there being no central processing facility, there being no requirement for any one node in the network to have knowledge of the entire network topology (nodes need only know about selected other nodes), and there being no requirement for a common communication facility. These features ensure that decentralised architectures are more robust, more scalable and more modular than centralised or distributed systems.
Unfortunately, decentralised tracking systems have, to date, only been able to implement simple tracking algorithms. Such systems are therefore unable to efficiently track targets executing complex manoeuvres. Distributed network architectures implementing IMM algorithms are known, for example from the paper “Centralised and Decentralised IMM Algorithms for Multisensor Track Fusion” by T. Ito and M. Faroq, published in the Proceedings of the Workshop on Estimation, Tracking and Fusion: A Tribute to Yaakob Bar-Shalom, Monterey Calif., 2001, which, in fact, does not disclose an IMM algorithm that is decentralised in the sense of the present application, but a distributed IMM algorithm which retains a centralised process to fuse estimates from remote sensing nodes. Each node performs an IMM procedure on locally generated data only, and a central fusion unit combines these resulting estimates. Such a process is not decentralised in the sense of the present invention, and therefore still exhibits the problems and disadvantages described above.